A Web-Based Application for Real-Time Malaria Prediction using Environmental Variables

Abstract Malaria remains a persistent public health challenge in Zimbabwe, particularly in rural districts such as Mudzi in Mashonaland East Province, where seasonal transmission and limited healthcare access undermine conventional control measures. This study presents the development and deployment of MalariaDash, a web-based application designed for real-time forecasting of malaria case counts using satellite-derived environmental data. The system integrates remote sensing inputs such as land surface temperature, vegetation indices, elevation, and proximity to water bodies with a Random Forest regression model trained on twenty-one months of health facility data. Predictor variables were selected based on their statistical significance in earlier modelling efforts. Unlike existing studies that focus on retrospective risk mapping or static models, MalariaDash operationalises machine learning outputs into an interactive platform. The application dynamically retrieves environmental data through Sentinel Hub and weather APIs, enabling location-specific and time-specific malaria predictions. It is implemented using the Django web framework and provides a user interface where environmental conditions and predicted malaria incidence can be viewed for selected dates and areas. This research demonstrates the practical value of linking environmental intelligence with predictive analytics in a rural African context. By delivering spatially explicit and near-real-time forecasts, MalariaDash enables health authorities to adopt proactive, targeted interventions rather than reactive responses. The approach illustrates a novel integration of geospatial modelling, machine learning, and operational web deployment aimed at improving local malaria control strategies. Author Summary In many rural parts of Zimbabwe, malaria continues to affect thousands of people every year. Although we know that changes in temperature, rainfall, and vegetation can influence where and when malaria outbreaks occur, local health workers often lack tools to make use of this information. In this study, we developed a web-based tool called MalariaDash that helps predict malaria cases using freely available environmental data from satellites. By combining these environmental signals with past malaria records, we created a system that allows users to select a location on a map and get a real-time forecast of malaria risk. What makes this tool different is that it is not just a research model, but it is designed for practical use by local health teams. We tested it in Mudzi District, a rural area with a high malaria burden in Zimbabwe, and the system performed well in identifying high-risk times and places. Our hope is that this approach can help health workers respond earlier and plan better, especially in areas where resources are limited and time matters most. Competing Interest Statement The authors have declared no competing interest. Funding Statement Yes Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: There was no need for an IRB. The malaria data used was aggregated/summary data and no individual data was used for the study. Additionally, the aggregated data was acquired from the Ministry of Health and Child Care aggregated at facility level. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The datasets used and/or analysed during the current study are not publicly available owing to restrictions by the Ministry of Health and Child Care but may be available from the corresponding author upon reasonable request.